{"total":64,"rows":[{"id":"2303.15361","title":"A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts","created":"2023-03-27","cited_by_count":359,"area":null},{"id":"2402.03358","title":"A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation","created":"2024-01-29","cited_by_count":57,"area":null},{"id":"2403.04468","title":"A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges","created":"2024-03-07","cited_by_count":54,"area":null},{"id":"2310.19035","title":"Does Invariant Graph Learning via Environment Augmentation Learn Invariance?","created":"2023-10-29","cited_by_count":42,"area":null},{"id":"2306.01323","title":"Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?","created":"2023-06-02","cited_by_count":41,"area":null},{"id":"2211.02843","title":"Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift","created":"2022-11-05","cited_by_count":38,"area":null},{"id":"2310.00800","title":"GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation","created":"2023-10-01","cited_by_count":31,"area":null},{"id":"2405.11868","title":"Towards Graph Contrastive Learning: A Survey and Beyond","created":"2024-05-20","cited_by_count":26,"area":null},{"id":"2403.01092","title":"Pairwise Alignment Improves Graph Domain Adaptation","created":"2024-03-02","cited_by_count":25,"area":null},{"id":"2403.07185","title":"Uncertainty in Graph Neural Networks: A Survey","created":"2024-03-11","cited_by_count":23,"area":null},{"id":"2310.04987","title":"Data-centric Graph Learning: A Survey","created":"2023-10-08","cited_by_count":22,"area":null},{"id":"2402.00447","title":"A Survey of Data-Efficient Graph Learning","created":"2024-02-01","cited_by_count":18,"area":null},{"id":"2402.16374","title":"Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning","created":"2024-02-26","cited_by_count":18,"area":null},{"id":"2401.06176","title":"GOODAT: Towards Test-time Graph Out-of-Distribution Detection","created":"2024-01-10","cited_by_count":17,"area":null},{"id":"2402.08907","title":"Subgraph Pooling: Tackling Negative Transfer on Graphs","created":"2024-02-14","cited_by_count":17,"area":null},{"id":"2410.19265","title":"A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation","created":"2024-10-25","cited_by_count":17,"area":null},{"id":"2505.12709","title":"Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics","created":"2025-05-19","cited_by_count":17,"area":null},{"id":"2407.15620","title":"Dual Test-time Training for Out-of-distribution Recommender System","created":"2024-07-22","cited_by_count":16,"area":null},{"id":"2403.01467","title":"Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation","created":"2024-03-03","cited_by_count":15,"area":null},{"id":"2402.08228","title":"Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective","created":"2024-02-13","cited_by_count":13,"area":null},{"id":"2406.07955","title":"How Interpretable Are Interpretable Graph Neural Networks?","created":"2024-06-12","cited_by_count":13,"area":null},{"id":"2403.09953","title":"Online GNN Evaluation Under Test-time Graph Distribution Shifts","created":"2024-03-15","cited_by_count":12,"area":null},{"id":"2511.07023","title":"Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time","created":"2025-11-10","cited_by_count":12,"area":null},{"id":"2410.06976","title":"Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation","created":"2024-10-09","cited_by_count":10,"area":null},{"id":"2407.19311","title":"Can Modifying Data Address Graph Domain Adaptation?","created":"2024-07-27","cited_by_count":9,"area":null},{"id":"2602.08431","title":"USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation","created":"2026-02-09","cited_by_count":9,"area":null},{"id":"2505.16860","title":"GCAL: Adapting Graph Models to Evolving Domain Shifts","created":"2025-05-22","cited_by_count":8,"area":null},{"id":"2402.11153","title":"Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs","created":"2024-02-17","cited_by_count":7,"area":null},{"id":"2405.08424","title":"Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More","created":"2024-05-14","cited_by_count":7,"area":null},{"id":"2306.06936","title":"CARL-G: Clustering-Accelerated Representation Learning on Graphs","created":"2023-06-12","cited_by_count":6,"area":null},{"id":"2406.05482","title":"Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks","created":"2024-06-08","cited_by_count":6,"area":null},{"id":"2409.08658","title":"Promoting Fairness in Link Prediction with Graph Enhancement","created":"2024-09-13","cited_by_count":6,"area":null},{"id":"2404.08660","title":"How Does Message Passing Improve Collaborative Filtering?","created":"2024-03-27","cited_by_count":5,"area":null},{"id":"2411.02847","title":"Dissecting the Failure of Invariant Learning on Graphs","created":"2024-11-05","cited_by_count":5,"area":null},{"id":"2310.13845","title":"Spectral-Aware Augmentation for Enhanced Graph Representation Learning","created":"2023-10-20","cited_by_count":4,"area":null},{"id":"2502.03033","title":"Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation","created":"2025-02-05","cited_by_count":4,"area":null},{"id":"2510.24039","title":"Geometric Algorithms for Neural Combinatorial Optimization with Constraints","created":"2025-10-28","cited_by_count":4,"area":null},{"id":"2404.13571","title":"Test-Time Training on Graphs with Large Language Models (LLMs)","created":"2024-04-21","cited_by_count":3,"area":null},{"id":"2502.14293","title":"Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision","created":"2025-02-20","cited_by_count":2,"area":null},{"id":"2502.18334","title":"Structural Alignment Improves Graph Test-Time Adaptation","created":"2025-02-25","cited_by_count":2,"area":null},{"id":"2308.09259","title":"FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction","created":"2023-08-18","cited_by_count":1,"area":null},{"id":"2410.09708","title":"Control the GNN: Utilizing Neural Controller with Lyapunov Stability for Test-Time Feature Reconstruction","created":"2024-10-13","cited_by_count":1,"area":null},{"id":"2503.18195","title":"Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2510.18363","title":"Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming","created":"2025-10-21","cited_by_count":1,"area":null},{"id":"2510.22289","title":"Does Homophily Help in Robust Test-time Node Classification?","created":"2025-10-25","cited_by_count":1,"area":null},{"id":"2602.17342","title":"From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection","created":"2026-02-19","cited_by_count":1,"area":null},{"id":"2605.14809","title":"GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2605.29526","title":"Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection","created":"2026-05-28","cited_by_count":1,"area":null},{"id":"2606.22914","title":"PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs","created":"2026-06-22","cited_by_count":1,"area":null},{"id":"2406.00764","title":"IENE: Identifying and Extrapolating the Node Environment for Out-of-Distribution Generalization on Graphs","created":"2024-06-02","cited_by_count":0,"area":null},{"id":"2406.08788","title":"Towards Understanding Link Predictor Generalizability Under Distribution Shifts","created":"2024-06-13","cited_by_count":0,"area":null},{"id":"2502.19670","title":"Training Robust Graph Neural Networks by Modeling Noise Dependencies","created":"2025-02-27","cited_by_count":0,"area":null},{"id":"2507.18804","title":"Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors","created":"2025-07-24","cited_by_count":0,"area":null},{"id":"2508.06734","title":"Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2510.07762","title":"From Noisy to Native: LLM-driven Graph Restoration for Test-Time Graph Domain Adaptation","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2602.09824","title":"PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolation","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2604.22031","title":"Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2605.05534","title":"Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2606.00808","title":"Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2607.10804","title":"When does distribution shift break graph neural networks calibration?","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2608.27948","title":"Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs","created":"2026-08-28","cited_by_count":0,"area":null},{"id":"2609.02497","title":"RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.34904","title":"Reference-Tail Trust:Certified Probability Floors for Learned Updates Inside a Deployed Network","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.02517","title":"Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}